Overview

Dataset statistics

Number of variables15
Number of observations5306
Missing cells3868
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory621.9 KiB
Average record size in memory120.0 B

Variable types

Categorical3
Numeric12

Alerts

Symbol has constant value "HDFC" Constant
Series has constant value "EQ" Constant
Date has a high cardinality: 5306 distinct values High cardinality
Prev Close is highly correlated with Open and 7 other fieldsHigh correlation
Open is highly correlated with Prev Close and 7 other fieldsHigh correlation
High is highly correlated with Prev Close and 7 other fieldsHigh correlation
Low is highly correlated with Prev Close and 7 other fieldsHigh correlation
Last is highly correlated with Prev Close and 7 other fieldsHigh correlation
Close is highly correlated with Prev Close and 7 other fieldsHigh correlation
VWAP is highly correlated with Prev Close and 7 other fieldsHigh correlation
Volume is highly correlated with Turnover and 2 other fieldsHigh correlation
Turnover is highly correlated with Prev Close and 9 other fieldsHigh correlation
Trades is highly correlated with Prev Close and 9 other fieldsHigh correlation
Deliverable Volume is highly correlated with Volume and 2 other fieldsHigh correlation
Prev Close is highly correlated with Open and 6 other fieldsHigh correlation
Open is highly correlated with Prev Close and 6 other fieldsHigh correlation
High is highly correlated with Prev Close and 6 other fieldsHigh correlation
Low is highly correlated with Prev Close and 6 other fieldsHigh correlation
Last is highly correlated with Prev Close and 6 other fieldsHigh correlation
Close is highly correlated with Prev Close and 6 other fieldsHigh correlation
VWAP is highly correlated with Prev Close and 6 other fieldsHigh correlation
Volume is highly correlated with Turnover and 1 other fieldsHigh correlation
Turnover is highly correlated with Volume and 2 other fieldsHigh correlation
Trades is highly correlated with Prev Close and 7 other fieldsHigh correlation
Deliverable Volume is highly correlated with Volume and 1 other fieldsHigh correlation
Prev Close is highly correlated with Open and 5 other fieldsHigh correlation
Open is highly correlated with Prev Close and 5 other fieldsHigh correlation
High is highly correlated with Prev Close and 5 other fieldsHigh correlation
Low is highly correlated with Prev Close and 5 other fieldsHigh correlation
Last is highly correlated with Prev Close and 5 other fieldsHigh correlation
Close is highly correlated with Prev Close and 5 other fieldsHigh correlation
VWAP is highly correlated with Prev Close and 5 other fieldsHigh correlation
Volume is highly correlated with Turnover and 2 other fieldsHigh correlation
Turnover is highly correlated with Volume and 2 other fieldsHigh correlation
Trades is highly correlated with Volume and 1 other fieldsHigh correlation
Deliverable Volume is highly correlated with Volume and 1 other fieldsHigh correlation
Series is highly correlated with SymbolHigh correlation
Symbol is highly correlated with SeriesHigh correlation
Prev Close is highly correlated with Open and 6 other fieldsHigh correlation
Open is highly correlated with Prev Close and 6 other fieldsHigh correlation
High is highly correlated with Prev Close and 6 other fieldsHigh correlation
Low is highly correlated with Prev Close and 6 other fieldsHigh correlation
Last is highly correlated with Prev Close and 6 other fieldsHigh correlation
Close is highly correlated with Prev Close and 6 other fieldsHigh correlation
VWAP is highly correlated with Prev Close and 6 other fieldsHigh correlation
Volume is highly correlated with Turnover and 1 other fieldsHigh correlation
Turnover is highly correlated with Volume and 2 other fieldsHigh correlation
Trades is highly correlated with Prev Close and 7 other fieldsHigh correlation
Deliverable Volume is highly correlated with Volume and 1 other fieldsHigh correlation
Trades has 2850 (53.7%) missing values Missing
Deliverable Volume has 509 (9.6%) missing values Missing
%Deliverble has 509 (9.6%) missing values Missing
Volume is highly skewed (γ1 = 28.95283232) Skewed
Deliverable Volume is highly skewed (γ1 = 40.95751616) Skewed
Date is uniformly distributed Uniform
Date has unique values Unique
Turnover has unique values Unique

Reproduction

Analysis started2022-05-25 12:15:30.447091
Analysis finished2022-05-25 12:15:52.633974
Duration22.19 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct5306
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size41.6 KiB
2000-01-03
 
1
2014-03-10
 
1
2014-03-06
 
1
2014-03-05
 
1
2014-03-04
 
1
Other values (5301)
5301 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters53060
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5306 ?
Unique (%)100.0%

Sample

1st row2000-01-03
2nd row2000-01-04
3rd row2000-01-05
4th row2000-01-06
5th row2000-01-07

Common Values

ValueCountFrequency (%)
2000-01-031
 
< 0.1%
2014-03-101
 
< 0.1%
2014-03-061
 
< 0.1%
2014-03-051
 
< 0.1%
2014-03-041
 
< 0.1%
2014-03-031
 
< 0.1%
2014-02-281
 
< 0.1%
2014-02-261
 
< 0.1%
2014-02-251
 
< 0.1%
2014-02-241
 
< 0.1%
Other values (5296)5296
99.8%

Length

2022-05-25T17:45:52.842980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2000-01-031
 
< 0.1%
2000-01-061
 
< 0.1%
2000-01-101
 
< 0.1%
2000-01-111
 
< 0.1%
2000-01-121
 
< 0.1%
2000-01-131
 
< 0.1%
2000-01-141
 
< 0.1%
2000-01-171
 
< 0.1%
2000-01-181
 
< 0.1%
2000-01-191
 
< 0.1%
Other values (5296)5296
99.8%

Most occurring characters

ValueCountFrequency (%)
015072
28.4%
-10612
20.0%
29262
17.5%
17603
14.3%
31759
 
3.3%
71494
 
2.8%
81471
 
2.8%
61460
 
2.8%
51450
 
2.7%
41440
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number42448
80.0%
Dash Punctuation10612
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015072
35.5%
29262
21.8%
17603
17.9%
31759
 
4.1%
71494
 
3.5%
81471
 
3.5%
61460
 
3.4%
51450
 
3.4%
41440
 
3.4%
91437
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
-10612
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common53060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015072
28.4%
-10612
20.0%
29262
17.5%
17603
14.3%
31759
 
3.3%
71494
 
2.8%
81471
 
2.8%
61460
 
2.8%
51450
 
2.7%
41440
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII53060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015072
28.4%
-10612
20.0%
29262
17.5%
17603
14.3%
31759
 
3.3%
71494
 
2.8%
81471
 
2.8%
61460
 
2.8%
51450
 
2.7%
41440
 
2.7%

Symbol
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.6 KiB
HDFC
5306 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters21224
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHDFC
2nd rowHDFC
3rd rowHDFC
4th rowHDFC
5th rowHDFC

Common Values

ValueCountFrequency (%)
HDFC5306
100.0%

Length

2022-05-25T17:45:52.940718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-25T17:45:53.050532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
hdfc5306
100.0%

Most occurring characters

ValueCountFrequency (%)
H5306
25.0%
D5306
25.0%
F5306
25.0%
C5306
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter21224
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H5306
25.0%
D5306
25.0%
F5306
25.0%
C5306
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21224
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H5306
25.0%
D5306
25.0%
F5306
25.0%
C5306
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII21224
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H5306
25.0%
D5306
25.0%
F5306
25.0%
C5306
25.0%

Series
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.6 KiB
EQ
5306 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10612
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEQ
2nd rowEQ
3rd rowEQ
4th rowEQ
5th rowEQ

Common Values

ValueCountFrequency (%)
EQ5306
100.0%

Length

2022-05-25T17:45:53.134308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-25T17:45:53.230770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
eq5306
100.0%

Most occurring characters

ValueCountFrequency (%)
E5306
50.0%
Q5306
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10612
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E5306
50.0%
Q5306
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10612
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E5306
50.0%
Q5306
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10612
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E5306
50.0%
Q5306
50.0%

Prev Close
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4875
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1283.666114
Minimum271.75
Maximum3180.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2022-05-25T17:45:53.330500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum271.75
5-th percentile460.9
Q1668.65
median1136.275
Q31811.475
95-th percentile2666.075
Maximum3180.15
Range2908.4
Interquartile range (IQR)1142.825

Descriptive statistics

Standard deviation709.3950897
Coefficient of variation (CV)0.5526320919
Kurtosis-0.6855939289
Mean1283.666114
Median Absolute Deviation (MAD)496.525
Skewness0.6661413288
Sum6811132.4
Variance503241.3934
MonotonicityNot monotonic
2022-05-25T17:45:53.459357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666.84
 
0.1%
659.854
 
0.1%
695.33
 
0.1%
375.13
 
0.1%
1296.453
 
0.1%
1315.353
 
0.1%
650.053
 
0.1%
668.553
 
0.1%
375.553
 
0.1%
6703
 
0.1%
Other values (4865)5274
99.4%
ValueCountFrequency (%)
271.751
< 0.1%
283.852
< 0.1%
285.61
< 0.1%
286.552
< 0.1%
287.21
< 0.1%
291.351
< 0.1%
292.81
< 0.1%
293.051
< 0.1%
293.51
< 0.1%
296.451
< 0.1%
ValueCountFrequency (%)
3180.151
< 0.1%
3169.41
< 0.1%
3131.651
< 0.1%
3126.51
< 0.1%
3116.31
< 0.1%
3115.551
< 0.1%
3112.11
< 0.1%
3080.51
< 0.1%
3076.851
< 0.1%
3073.21
< 0.1%

Open
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3502
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1284.393074
Minimum284
Maximum3148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2022-05-25T17:45:53.595196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum284
5-th percentile460
Q1669.7125
median1135.4
Q31813.8125
95-th percentile2668
Maximum3148
Range2864
Interquartile range (IQR)1144.1

Descriptive statistics

Standard deviation709.7036653
Coefficient of variation (CV)0.5525595549
Kurtosis-0.6920671877
Mean1284.393074
Median Absolute Deviation (MAD)496.5
Skewness0.6641021859
Sum6814989.65
Variance503679.2925
MonotonicityNot monotonic
2022-05-25T17:45:53.723852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66019
 
0.4%
63016
 
0.3%
67015
 
0.3%
65015
 
0.3%
66514
 
0.3%
63513
 
0.2%
64011
 
0.2%
68011
 
0.2%
37511
 
0.2%
68511
 
0.2%
Other values (3492)5170
97.4%
ValueCountFrequency (%)
2841
 
< 0.1%
2851
 
< 0.1%
2871
 
< 0.1%
2881
 
< 0.1%
2903
0.1%
2922
< 0.1%
293.51
 
< 0.1%
2981
 
< 0.1%
3011
 
< 0.1%
3021
 
< 0.1%
ValueCountFrequency (%)
31481
< 0.1%
31401
< 0.1%
31301
< 0.1%
31251
< 0.1%
3116.11
< 0.1%
3084.81
< 0.1%
30802
< 0.1%
30791
< 0.1%
3073.051
< 0.1%
30731
< 0.1%

High
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3950
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1304.269732
Minimum290.5
Maximum3262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2022-05-25T17:45:53.859759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum290.5
5-th percentile470.0125
Q1677.5125
median1156.725
Q31835
95-th percentile2715
Maximum3262
Range2971.5
Interquartile range (IQR)1157.4875

Descriptive statistics

Standard deviation721.3080797
Coefficient of variation (CV)0.5530359724
Kurtosis-0.6861489538
Mean1304.269732
Median Absolute Deviation (MAD)507.375
Skewness0.6685128138
Sum6920455.2
Variance520285.3458
MonotonicityNot monotonic
2022-05-25T17:45:53.988628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65015
 
0.3%
69513
 
0.2%
63012
 
0.2%
67512
 
0.2%
67011
 
0.2%
66010
 
0.2%
20009
 
0.2%
6489
 
0.2%
6659
 
0.2%
6558
 
0.2%
Other values (3940)5198
98.0%
ValueCountFrequency (%)
290.51
< 0.1%
2921
< 0.1%
2931
< 0.1%
293.51
< 0.1%
2941
< 0.1%
2961
< 0.1%
296.351
< 0.1%
303.91
< 0.1%
3062
< 0.1%
3071
< 0.1%
ValueCountFrequency (%)
32621
< 0.1%
3219.71
< 0.1%
32151
< 0.1%
3199.91
< 0.1%
31901
< 0.1%
3158.51
< 0.1%
31481
< 0.1%
31451
< 0.1%
3139.71
< 0.1%
3132.251
< 0.1%

Low
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4195
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1263.297842
Minimum273.25
Maximum3100.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2022-05-25T17:45:54.122270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum273.25
5-th percentile448.5
Q1660
median1119
Q31783.075
95-th percentile2619.75
Maximum3100.55
Range2827.3
Interquartile range (IQR)1123.075

Descriptive statistics

Standard deviation697.4503093
Coefficient of variation (CV)0.5520869949
Kurtosis-0.6905357875
Mean1263.297842
Median Absolute Deviation (MAD)490.725
Skewness0.6613471339
Sum6703058.35
Variance486436.9339
MonotonicityNot monotonic
2022-05-25T17:45:54.255575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66016
 
0.3%
66513
 
0.2%
62512
 
0.2%
63311
 
0.2%
64010
 
0.2%
6109
 
0.2%
6709
 
0.2%
5409
 
0.2%
6358
 
0.2%
6757
 
0.1%
Other values (4185)5202
98.0%
ValueCountFrequency (%)
273.251
< 0.1%
276.251
< 0.1%
2812
< 0.1%
2831
< 0.1%
284.51
< 0.1%
2852
< 0.1%
2881
< 0.1%
2901
< 0.1%
2911
< 0.1%
293.21
< 0.1%
ValueCountFrequency (%)
3100.551
< 0.1%
30651
< 0.1%
3060.851
< 0.1%
3050.51
< 0.1%
3044.251
< 0.1%
30402
< 0.1%
3030.31
< 0.1%
3025.051
< 0.1%
3022.11
< 0.1%
30201
< 0.1%

Last
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4046
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1283.885017
Minimum282.85
Maximum3178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2022-05-25T17:45:54.389904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum282.85
5-th percentile462
Q1669
median1135
Q31812
95-th percentile2665.15
Maximum3178
Range2895.15
Interquartile range (IQR)1143

Descriptive statistics

Standard deviation709.2502038
Coefficient of variation (CV)0.5524250181
Kurtosis-0.688907382
Mean1283.885017
Median Absolute Deviation (MAD)496.25
Skewness0.6652456259
Sum6812293.9
Variance503035.8516
MonotonicityNot monotonic
2022-05-25T17:45:54.519526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67018
 
0.3%
65014
 
0.3%
66014
 
0.3%
70011
 
0.2%
63011
 
0.2%
37510
 
0.2%
64510
 
0.2%
6659
 
0.2%
6409
 
0.2%
6759
 
0.2%
Other values (4036)5191
97.8%
ValueCountFrequency (%)
282.851
< 0.1%
2841
< 0.1%
285.251
< 0.1%
287.11
< 0.1%
288.41
< 0.1%
2911
< 0.1%
291.91
< 0.1%
293.21
< 0.1%
293.51
< 0.1%
2941
< 0.1%
ValueCountFrequency (%)
31781
< 0.1%
3155.51
< 0.1%
31351
< 0.1%
3122.851
< 0.1%
31201
< 0.1%
31151
< 0.1%
31001
< 0.1%
30801
< 0.1%
30791
< 0.1%
30681
< 0.1%

Close
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4875
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1284.071005
Minimum283.85
Maximum3180.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2022-05-25T17:45:54.647185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum283.85
5-th percentile461.1125
Q1668.6625
median1136.675
Q31811.7875
95-th percentile2666.075
Maximum3180.15
Range2896.3
Interquartile range (IQR)1143.125

Descriptive statistics

Standard deviation709.4305146
Coefficient of variation (CV)0.5524854249
Kurtosis-0.6871177606
Mean1284.071005
Median Absolute Deviation (MAD)496.75
Skewness0.665651107
Sum6813280.75
Variance503291.655
MonotonicityNot monotonic
2022-05-25T17:45:54.769888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666.84
 
0.1%
659.854
 
0.1%
662.353
 
0.1%
650.053
 
0.1%
6703
 
0.1%
375.553
 
0.1%
668.553
 
0.1%
669.23
 
0.1%
644.33
 
0.1%
695.33
 
0.1%
Other values (4865)5274
99.4%
ValueCountFrequency (%)
283.852
< 0.1%
285.61
< 0.1%
286.552
< 0.1%
287.21
< 0.1%
291.351
< 0.1%
292.81
< 0.1%
293.051
< 0.1%
293.51
< 0.1%
296.451
< 0.1%
299.21
< 0.1%
ValueCountFrequency (%)
3180.151
< 0.1%
3169.41
< 0.1%
3131.651
< 0.1%
3126.51
< 0.1%
3116.31
< 0.1%
3115.551
< 0.1%
3112.11
< 0.1%
3080.51
< 0.1%
3076.851
< 0.1%
3073.21
< 0.1%

VWAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5215
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1283.664578
Minimum283.6
Maximum3166.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2022-05-25T17:45:54.902534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum283.6
5-th percentile459.8625
Q1668.265
median1136.72
Q31811.68
95-th percentile2670.0725
Maximum3166.58
Range2882.98
Interquartile range (IQR)1143.415

Descriptive statistics

Standard deviation709.1096218
Coefficient of variation (CV)0.5524103679
Kurtosis-0.6897231115
Mean1283.664578
Median Absolute Deviation (MAD)497.205
Skewness0.6646488728
Sum6811124.25
Variance502836.4557
MonotonicityNot monotonic
2022-05-25T17:45:55.156857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
665.433
 
0.1%
664.563
 
0.1%
649.552
 
< 0.1%
1128.592
 
< 0.1%
639.392
 
< 0.1%
2327.262
 
< 0.1%
568.682
 
< 0.1%
615.562
 
< 0.1%
632.632
 
< 0.1%
1350.942
 
< 0.1%
Other values (5205)5284
99.6%
ValueCountFrequency (%)
283.61
< 0.1%
284.541
< 0.1%
285.841
< 0.1%
287.61
< 0.1%
288.81
< 0.1%
289.421
< 0.1%
293.51
< 0.1%
294.531
< 0.1%
295.581
< 0.1%
297.571
< 0.1%
ValueCountFrequency (%)
3166.581
< 0.1%
3144.141
< 0.1%
3123.721
< 0.1%
3113.81
< 0.1%
3096.661
< 0.1%
3096.411
< 0.1%
3094.321
< 0.1%
3090.931
< 0.1%
3086.751
< 0.1%
3084.31
< 0.1%

Volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct5302
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1848186.916
Minimum2919
Maximum158414118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2022-05-25T17:45:55.289499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2919
5-th percentile37367
Q1303497
median1337788
Q32732309.75
95-th percentile5320186.5
Maximum158414118
Range158411199
Interquartile range (IQR)2428812.75

Descriptive statistics

Standard deviation2991387.057
Coefficient of variation (CV)1.618552232
Kurtosis1441.593429
Mean1848186.916
Median Absolute Deviation (MAD)1120539.5
Skewness28.95283232
Sum9806479776
Variance8.948396526 × 1012
MonotonicityNot monotonic
2022-05-25T17:45:55.422144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
699582
 
< 0.1%
190792
 
< 0.1%
2055132
 
< 0.1%
2686922
 
< 0.1%
33260281
 
< 0.1%
37664751
 
< 0.1%
34941571
 
< 0.1%
17241261
 
< 0.1%
12757951
 
< 0.1%
11356291
 
< 0.1%
Other values (5292)5292
99.7%
ValueCountFrequency (%)
29191
< 0.1%
38151
< 0.1%
38351
< 0.1%
46091
< 0.1%
46181
< 0.1%
46741
< 0.1%
47971
< 0.1%
50121
< 0.1%
51781
< 0.1%
54311
< 0.1%
ValueCountFrequency (%)
1584141181
< 0.1%
597574091
< 0.1%
200842901
< 0.1%
190908211
< 0.1%
182036361
< 0.1%
168296281
< 0.1%
148002931
< 0.1%
141736371
< 0.1%
138091031
< 0.1%
137808491
< 0.1%

Turnover
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct5306
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.620758699 × 1014
Minimum1.8346859 × 1011
Maximum1.043772976 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2022-05-25T17:45:55.565163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.8346859 × 1011
5-th percentile2.124333116 × 1012
Q12.461178081 × 1013
median1.684207413 × 1014
Q33.419282205 × 1014
95-th percentile9.089071929 × 1014
Maximum1.043772976 × 1016
Range1.043754629 × 1016
Interquartile range (IQR)3.173164397 × 1014

Descriptive statistics

Standard deviation3.607843745 × 1014
Coefficient of variation (CV)1.37664095
Kurtosis129.4304989
Mean2.620758699 × 1014
Median Absolute Deviation (MAD)1.495277823 × 1014
Skewness6.515693225
Sum1.390574566 × 1018
Variance1.301653649 × 1029
MonotonicityNot monotonic
2022-05-25T17:45:55.701559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.675364 × 10111
 
< 0.1%
3.185017044 × 10141
 
< 0.1%
1.438820013 × 10141
 
< 0.1%
1.047182033 × 10141
 
< 0.1%
9.323497284 × 10131
 
< 0.1%
9.348680494 × 10131
 
< 0.1%
2.721024306 × 10141
 
< 0.1%
1.679587032 × 10141
 
< 0.1%
9.118475968 × 10131
 
< 0.1%
6.17100138 × 10131
 
< 0.1%
Other values (5296)5296
99.8%
ValueCountFrequency (%)
1.8346859 × 10111
< 0.1%
2.2743175 × 10111
< 0.1%
2.4179542 × 10111
< 0.1%
2.5561039 × 10111
< 0.1%
2.67377335 × 10111
< 0.1%
2.95503785 × 10111
< 0.1%
2.971299 × 10111
< 0.1%
2.9971248 × 10111
< 0.1%
3.1632082 × 10111
< 0.1%
3.17231295 × 10111
< 0.1%
ValueCountFrequency (%)
1.043772976 × 10161
< 0.1%
4.538977828 × 10151
< 0.1%
3.710496476 × 10151
< 0.1%
3.192429856 × 10151
< 0.1%
2.788764375 × 10151
< 0.1%
2.500699737 × 10151
< 0.1%
2.446626951 × 10151
< 0.1%
2.280150632 × 10151
< 0.1%
2.263437782 × 10151
< 0.1%
2.246614591 × 10151
< 0.1%

Trades
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2441
Distinct (%)99.4%
Missing2850
Missing (%)53.7%
Infinite0
Infinite (%)0.0%
Mean102159.0513
Minimum973
Maximum538170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2022-05-25T17:45:55.828189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum973
5-th percentile34196.25
Q162011.5
median90405
Q3129754.5
95-th percentile207512.25
Maximum538170
Range537197
Interquartile range (IQR)67743

Descriptive statistics

Standard deviation57948.6032
Coefficient of variation (CV)0.5672390499
Kurtosis4.429716009
Mean102159.0513
Median Absolute Deviation (MAD)32212.5
Skewness1.575754281
Sum250902630
Variance3358040613
MonotonicityNot monotonic
2022-05-25T17:45:55.962830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1192882
 
< 0.1%
530122
 
< 0.1%
905272
 
< 0.1%
1097692
 
< 0.1%
1016682
 
< 0.1%
981262
 
< 0.1%
611962
 
< 0.1%
1072052
 
< 0.1%
958002
 
< 0.1%
631292
 
< 0.1%
Other values (2431)2436
45.9%
(Missing)2850
53.7%
ValueCountFrequency (%)
9731
< 0.1%
9951
< 0.1%
10171
< 0.1%
13381
< 0.1%
14441
< 0.1%
14971
< 0.1%
29121
< 0.1%
32091
< 0.1%
33101
< 0.1%
34791
< 0.1%
ValueCountFrequency (%)
5381701
< 0.1%
4547581
< 0.1%
4104601
< 0.1%
3886751
< 0.1%
3871541
< 0.1%
3829981
< 0.1%
3746461
< 0.1%
3652811
< 0.1%
3608371
< 0.1%
3427351
< 0.1%

Deliverable Volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct4790
Distinct (%)99.9%
Missing509
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean1329439.551
Minimum1786
Maximum148313109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2022-05-25T17:45:56.096472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1786
5-th percentile55209.4
Q1268807
median1021574
Q31939101
95-th percentile3606496.4
Maximum148313109
Range148311323
Interquartile range (IQR)1670294

Descriptive statistics

Standard deviation2555073.431
Coefficient of variation (CV)1.92191772
Kurtosis2300.642621
Mean1329439.551
Median Absolute Deviation (MAD)792177
Skewness40.95751616
Sum6377321528
Variance6.528400238 × 1012
MonotonicityNot monotonic
2022-05-25T17:45:56.223133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15003112
 
< 0.1%
22857252
 
< 0.1%
693382
 
< 0.1%
5272032
 
< 0.1%
19365032
 
< 0.1%
54942
 
< 0.1%
24292192
 
< 0.1%
12841151
 
< 0.1%
8325021
 
< 0.1%
13660611
 
< 0.1%
Other values (4780)4780
90.1%
(Missing)509
 
9.6%
ValueCountFrequency (%)
17861
< 0.1%
28001
< 0.1%
30461
< 0.1%
32651
< 0.1%
37991
< 0.1%
38851
< 0.1%
39551
< 0.1%
41821
< 0.1%
42451
< 0.1%
43801
< 0.1%
ValueCountFrequency (%)
1483131091
< 0.1%
441866741
< 0.1%
161536191
< 0.1%
124697781
< 0.1%
118838041
< 0.1%
117620971
< 0.1%
103891231
< 0.1%
102434001
< 0.1%
101767781
< 0.1%
101011511
< 0.1%

%Deliverble
Real number (ℝ≥0)

MISSING

Distinct2992
Distinct (%)62.4%
Missing509
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean0.6530880967
Minimum0.119
Maximum0.9894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2022-05-25T17:45:56.354781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.119
5-th percentile0.4176
Q10.5752
median0.6669
Q30.742
95-th percentile0.84002
Maximum0.9894
Range0.8704
Interquartile range (IQR)0.1668

Descriptive statistics

Standard deviation0.1281145917
Coefficient of variation (CV)0.1961673967
Kurtosis0.2589373264
Mean0.6530880967
Median Absolute Deviation (MAD)0.0824
Skewness-0.4534727608
Sum3132.8636
Variance0.01641334862
MonotonicityNot monotonic
2022-05-25T17:45:56.484436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.61127
 
0.1%
0.72937
 
0.1%
0.69466
 
0.1%
0.66186
 
0.1%
0.66556
 
0.1%
0.68236
 
0.1%
0.5995
 
0.1%
0.6725
 
0.1%
0.6595
 
0.1%
0.78785
 
0.1%
Other values (2982)4739
89.3%
(Missing)509
 
9.6%
ValueCountFrequency (%)
0.1191
< 0.1%
0.15571
< 0.1%
0.1851
< 0.1%
0.18531
< 0.1%
0.19421
< 0.1%
0.22061
< 0.1%
0.22271
< 0.1%
0.22471
< 0.1%
0.22661
< 0.1%
0.22991
< 0.1%
ValueCountFrequency (%)
0.98941
< 0.1%
0.98561
< 0.1%
0.9781
< 0.1%
0.97761
< 0.1%
0.97681
< 0.1%
0.97361
< 0.1%
0.97141
< 0.1%
0.96961
< 0.1%
0.96831
< 0.1%
0.96781
< 0.1%

Interactions

2022-05-25T17:45:50.295938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:33.696520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:35.209738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:36.783755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:38.212827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:39.771504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:41.190226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:42.737835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:44.153939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:45.669966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:47.242708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:48.644887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:50.408461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:33.845430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:35.326363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:36.903400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:38.328486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:39.890185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:41.308938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:42.854518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:44.281600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:45.787652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:47.352573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:48.767384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:50.524121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:33.959486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:35.439477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:37.021642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:38.445204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:40.008868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:41.422637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:42.971210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:44.405267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:46.042687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:47.463339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:48.891353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:50.637817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:34.075147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:35.560182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:37.139359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:38.561862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:40.126061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:41.540322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:43.087865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:44.531897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:46.159768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:47.576746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:49.019875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:50.751313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:34.200843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:35.675872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:37.255790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:38.684179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:40.238761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:41.655013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:43.207052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:44.653513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:46.276422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:47.687447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:49.143512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:50.864976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:34.317256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:35.792811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:37.373125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:38.798085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:40.354451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:41.769676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:43.320490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:44.779414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:46.394137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:47.801144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:49.404815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:50.979703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:34.430327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:36.071820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:37.491840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:38.916515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:40.470110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:41.886540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:43.437515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:44.903104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:46.511791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:47.911850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:49.529035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:51.095390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:34.607090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:36.186041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:37.605534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:39.035408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:40.588145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:42.006788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:43.555169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:45.027752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:46.629599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:48.027539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:49.653829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:51.221023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:34.736997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:36.315508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:37.735186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:39.165088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:40.714527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:42.133448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:43.681860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:45.162358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:46.756010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:48.156195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:49.787178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:51.335716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:34.856125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:36.432041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:37.855088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:39.417450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:40.831216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:42.251137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:43.798078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:45.286988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:46.875655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:48.274878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:49.913533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:51.461062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:34.969074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:36.543753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:37.972470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:39.533171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:40.946875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:42.362840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:43.914248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:45.412648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:46.997330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:48.392531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:50.042219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:51.586690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:35.094773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:36.670053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:38.099130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:39.658839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:41.075562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:42.488468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:44.040214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:45.549287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:47.128979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:48.520221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:45:50.174569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-05-25T17:45:56.604116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-25T17:45:56.822467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-25T17:45:57.010031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-25T17:45:57.179882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-25T17:45:57.427222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-25T17:45:51.861514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-25T17:45:52.199812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-25T17:45:52.378234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-25T17:45:52.493935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DateSymbolSeriesPrev CloseOpenHighLowLastCloseVWAPVolumeTurnoverTradesDeliverable Volume%Deliverble
02000-01-03HDFCEQ271.75293.5293.50293.50293.50293.50293.50227446.675364e+11NaNNaNNaN
12000-01-04HDFCEQ293.50317.0317.00297.00304.00304.05303.622552517.749972e+12NaNNaNNaN
22000-01-05HDFCEQ304.05290.0303.90285.00295.00292.80294.532690877.925368e+12NaNNaNNaN
32000-01-06HDFCEQ292.80301.0314.00295.00296.00296.45300.143059169.181669e+12NaNNaNNaN
42000-01-07HDFCEQ296.45290.0296.35281.00287.10286.55288.801970395.690480e+12NaNNaNNaN
52000-01-10HDFCEQ286.55292.0296.00285.00288.40287.20289.421333633.859779e+12NaNNaNNaN
62000-01-11HDFCEQ287.20290.0292.00273.25282.85283.85284.543374119.600617e+12NaNNaNNaN
72000-01-12HDFCEQ283.85287.0293.00284.50285.25285.60287.602225376.400217e+12NaNNaNNaN
82000-01-13HDFCEQ285.60288.0290.50283.00284.00283.85285.841132383.236741e+12NaNNaNNaN
92000-01-14HDFCEQ283.85284.0294.00276.25291.00286.55283.601523224.319905e+12NaNNaNNaN

Last rows

DateSymbolSeriesPrev CloseOpenHighLowLastCloseVWAPVolumeTurnoverTradesDeliverable Volume%Deliverble
52962021-04-16HDFCEQ2547.152550.002589.802546.852569.002574.052572.3931333118.060093e+14126445.01827639.00.5833
52972021-04-19HDFCEQ2574.052474.002510.002452.002501.052492.352484.2635508908.821343e+14150346.01875490.00.5282
52982021-04-20HDFCEQ2492.352542.152544.002406.202409.002415.902454.6873657651.808059e+15193822.04822407.00.6547
52992021-04-22HDFCEQ2415.902400.002485.002373.002478.052479.702437.4645147401.100450e+15234101.02618593.00.5800
53002021-04-23HDFCEQ2479.702455.052504.152437.502491.002497.352479.6531437797.795465e+14133007.01514578.00.4818
53012021-04-26HDFCEQ2497.352500.002534.102483.202502.002509.802508.0739160889.821805e+14121028.02440395.00.6232
53022021-04-27HDFCEQ2509.802494.152526.802486.252514.002518.402509.1820407995.120730e+14102250.01040749.00.5100
53032021-04-28HDFCEQ2518.402516.102609.002508.302575.002577.002574.2134074618.771527e+14117425.01815110.00.5327
53042021-04-29HDFCEQ2577.002590.902628.002533.002539.702538.852569.6530054687.722995e+14132826.01472924.00.4901
53052021-04-30HDFCEQ2538.852503.102525.002411.102433.252420.102445.9460245951.473581e+15224454.03839105.00.6372